Monitoring of Kubernetes platforms
Implementing end-to-end observability for dynamic environments
Auto-Scaling requires Auto-Monitoring
Dynamic IT infrastructures are characterised by the fact that they can quickly scale IT resources for applications up or down according to the needs of the business departments. But no matter to what extent the resources are available: They must be properly monitored and logged.
A company in the automotive industry was faced with the challenge of keeping a dynamically scaling system transparent. The dynamics resulted from multiple autoscaling infrastructures and a complex, shifting micro-service application. Therefore, the manual effort for the long-term storage of observation data had to be reduced to a minimum and at the same time be cost-efficient.
An legacy monitoring and logging stack, which did not meet the requirements, as well as a lack of standards in the entire system posed central hurdles
- The monitoring and logging system had to recognize scalings and was not allowed to trigger false-positive alarms
- Traditional surveillance systems rely on host names or IP addresses, which are static information that does not change. In dynamic environments these parameters sometimes change very often, which is why this information could not be used
- The monitoring and logging systems had to be scalable with the use of different AWS accounts. Therefore, the monitoring systems required autoscaling functions